I’m a freshly-minted Data Scientist, straight out of my Master’s in Data Science for Public Policy from Georgetown’s McCourt School.
These days, you’ll find me at the World Bank, working my data magic for Data360, where I help turn curate data that is used by millions across the globe.
Read on to learn more about me!
Psst…let’s connect on other platforms!
* LinkdIn
* Resume
The question you might ask is, why should we hire you?
Well, here’s all the treasure trove of skills I can bring to the table:
Most important of all is this however:
There will always be some knowledge and skills that I am not
well-versed in, situations where I am unaware of proceedings…but this
does not deter me. In fact, I am driven to learn new things and push
myself out of my comfort zone; an invaluable asset for anyone I work
with!
Throughout my career, I’ve had the opportunity to work across diverse
roles and environments, each bringing new perspectives and challenges.
Yet, two themes have remained constant:
A Commitment to Policy Impact – I’m deeply driven by the desire
to create positive change through data-driven policy. Whether I’m
developing new data insights or working hands-on with stakeholders, my
ultimate goal is to contribute to meaningful and informed policy
decisions.
A Passion for Data and Collaboration – Data is my foundation, but connecting with people is what brings my work to life. I thrive in roles where I can not only analyze data but also engage directly with teams and partners to communicate insights, build solutions, and foster shared understanding.
MS – Data Science And Public Policy, Georgetown University
Aug 22–May 24
Accolades:
Tech & Public Policy Scholar (100% Scholarship and RAship)
Leadership Positions:
1. Data Science and Public Policy Representative (McCourt Student Association)
2. Data Science Bootcamp Leader
BSc – Economics and Mathematics, Lahore University of Management Sciences
Aug 16–May 20
Accolades:
1. Valedictorian (Class of 2020)
2. Dean's Honor List (2016-2019)
3. Best Presented Paper in Category "Structural Transformation", South Asian Economics Students' Meet, 2023 (Colombo, Sri Lanka)
Leadership Positions:
1. Vice President, LUMS Daily Student
2. Peer Mentor
3. Teaching Assistants
With a variety of projects under my belt, I’ve gained valuable
experience and insights across different domains.
Let’s dive in and explore some highlights…
I became interested in how large companies play a role in the
policies that govern our every daily lives.
The questions I was
curious about were:
1. To what extent do these companies influence
the bills that pass through Congress?
2. Who are the large players
in this context, and which issues are most important to them?
3. Are there instances where these companies work together on several
bills, related to a certain field?
4. Are there other patterns? For
example, are some companies more prone to providing support to a certain
party to propel their interests?
To answer these, I scraped data provided by OpenSecrets.org to get
information about Congressional bills and their sponsors. I then merged
the data with other details about the bill, and finally performed some
detailed data analysis.
Skills used:
* Web Scraping
* Data manipulation
* PCA
& Cluster Analysis using K-Means
* Network Analysis using
networkx
I was also deeply interested in the role big technology firms play in
lobbying. Are there certain policy areas where they are more active,
e.g. internet regulation? Do they collaborate on a large number of
bills, or not? For that purpose, I made a specific dashboard that
highlights the results here:
Lastly, I wanted to present my findings in an interesting and
engaging way. What better way to do that, than in a game?!
In this,
I made a Shiny application where the user is provided ‘hints’ in the
form of data visualizations, and they have to guess which firm is being
talked about.
The idea was to get users to think about the many
different ways these firms are affecting the policies that govern our
lives.
Skills used:
* Shiny
* Plotly
Carbon emissions driving global warming have led to significant
changes worldwide, including accelerated deforestation in Bolivia.
To quantify this, we analyzed satellite images from the past seven
years, applying image segmentation to pinpoint deforested areas in
detail. Using a U-Net convolutional encoder-decoder model, originally
developed for medical imaging but highly effective in forestry
segmentation (achieving ~94% accuracy), we created binary
forest/non-forest masks for high-resolution satellite images from Planet
and estimated deforestation rates.
The U-Net architecture enabled feature extraction and mask creation by compressing spatial information and then reconstructing detailed classifications. After masking test data images, we calculated the forested area percentage for each region, finding the most significant change around quad ‘680-930’ (16.21° S, 63.37° W). Future work could explore more nuanced classifications beyond binary segmentation.
Final Outputs: * Slides * Github
Skills used:
* tensorflow
* keras
* matplotlib
As part of a group project, I conducted an analysis of
climate-related misinformation on Reddit, focusing on various subreddits
to address specific policy-related questions, using AWS as a
computational platform.
Our approach involved comparing techniques like data oversampling and
undersampling to address class imbalances, along with Logistic
Regression and XGBoost for the classification of climate denialism.
Leveraging PyTorch and the Simple Transformers library, we loaded a
pre-trained RoBERTa model to classify climate denial claims effectively.
We created visualizations to highlight key metrics, ensuring clear communication of technical findings to non-technical stakeholders. Additionally, we conducted a network analysis to identify key contributors and super-spreaders of climate denial claims, providing insights into the patterns of misinformation spread.
Final Results:
Skills used:
* AWS S3 Buckets
* Amazon ECS & Docker
* PySpark
* Pytorch
* R Markdown
* Networkx
*
ggplot
* matplotlib